Tutorials¶
Scikit-eo provides a rich suite of algorithms specifically designed for environmental studies. These include statistical analysis, machine learning, deep learning, data fusion and spatial analysis. Researchers can leverage these tools to explore patterns, relationships, and trends within their datasets, to uncover complex land or forest degradation or mapping and classify the land cover, and generate insightful visualizations, among others tools.
Scikit-eo tutorials notebooks¶
Remote sensing tools of scikit-eo can be mainly grouped into 3 sets.
Tools for remote sensing data analysis¶
2 Estimated area and uncertainty in Machine Learning.ipynb
3 Calibrating supervised classification in Remote Sensing.ipynb
5 Fusion of radar and optical images.ipynb
6 Spectral Mixture Analysis.ipynb
7 Principal Components Analysis.ipynb
8 Tasseled-Cap Transformation.ipynb
10 Logistic regression in remote sensing.ipynb
11 Deep Learning Classification FullyConnected.ipynb
Tools for satellite image preprocessing¶
12 Atmospheric Correction.ipynb
14 Writing a satellite image (raster).ipynb